AI decision-support platform for U.S. defense and national security
Rhombus Power builds AI-powered decision-support systems for government and defense sectors, processing multi-domain data (text, video, overhead imagery) through predictive models. The tech stack is heavy on data infrastructure—Python, Pandas, NumPy, PostgreSQL, MongoDB, HPC, ArcGIS—paired with containerization (Docker, Kubernetes adoption). Hiring velocity is accelerating across engineering and data roles, while active projects focus on high-value defense deployments and intelligence assessments; the pain-point pattern (security compliance gaps, annotation workforce scaling, labeling quality) suggests the core friction is operationalizing complex AI at enterprise scale in security-constrained environments.
Notable leadership hires: Head of Sales, Country Lead, Head of Strategic Growth
Rhombus Power delivers real-time AI-powered decision support to U.S. Government agencies and international defense partners. The platform unifies multi-domain data sources, AI modeling, and human expertise to enable faster, more informed decision-making on national security challenges. Founded in 2011 and based in Palo Alto, the company operates across 51–200 employees with distributed hiring across the United States, India, Philippines, Taiwan, Japan, and the United Kingdom. Active work includes large-scale solution deployments, predictive model operations, and strategic partnerships with defense ministries.
Python, Java, Pandas, NumPy, PostgreSQL, MongoDB, AWS, Azure, GCP, Docker, Kubernetes, HPC, ArcGIS, and QGIS. The stack emphasizes data processing, containerization, and geospatial analysis.
Palo Alto, California. The company hires across six countries: United States, India, Philippines, Taiwan, Japan, and United Kingdom.
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Rhombus Power Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.